title: Artificial neural network is highly predictive of outcome in paediatric acute liver failure.
abstract: Current prognostic models in PALF are unreliable, failing to account for complex, non-linear relationships existing between multiple prognostic factors. A computational approach using ANN should provide superior modelling to PELD-MELD scores. We assessed the prognostic accuracy of PELD-MELD scores and ANN in PALF in children presenting to the QLTS, Australia. A comprehensive registry-based data set was evaluated in 54 children (32M, 22F, median age 17 month) with PALF. PELD-MELD scores calculated at (i) meeting PALF criteria and (ii) peak. ANN was evaluated using stratified 10-fold cross-validation. Outcomes were classified as good (transplant-free survival) or poor (death or LT) and predictive accuracy compared using AUROC curves. Mean PELD-MELD scores were significantly higher in non-transplanted non-survivors (i) 37 and (ii) 46 and transplant recipients (i) 32 and (ii) 43 compared to transplant-free survivors (i) 26 and (ii) 30. Threshold PELD-MELD scores ≥27 and ≥42, at meeting PALF criteria and peak, gave AUROC 0.71 and 0.86, respectively, for poor outcome. ANN showed superior prediction for poor outcome with AUROC 0.96, sensitivity 82.6%, specificity 96%, PPV 96.2% and NPV 85.7% (cut-off 0.5). ANN is superior to PELD-MELD for predicting poor outcome in PALF.
authors.affiliation: Department of Pediatric Gastroenterology Hepatology and Nutrition, Starship Children's Hospital, Auckland, New Zealand.
authors.forename: J
authors.initials: J
authors.lastname: Rajanayagam
authors.forename: E
authors.initials: E
authors.lastname: Frank
authors.forename: R W
authors.initials: RW
authors.lastname: Shepherd
authors.forename: P J
authors.initials: PJ
authors.lastname: Lewindon
mesh_terms: D000293:Adolescent; D019540:Area Under Curve; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D058625:End Stage Liver Disease / Q000175:diagnosis; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D017093:Liver Failure / Q000628:therapy*; D017114:Liver Failure, Acute / Q000628:therapy*; D016031:Liver Transplantation / Q000379:methods*; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D016896:Treatment Outcome
keywords: acute liver failure; artificial neural network; pediatric end-stage liver disease score; pediatric liver transplantation